Genomics and Phylogenetic Studies

DUMMEST improves frameshift-aware alignment of DNA sequences to protein families by combining simplicity and speed, outperforming existing methods in sensitivity and specificity.

The paper evaluates normalization methods and quantification pipelines for long-read RNA sequencing across PacBio and ONT platforms, showing platform-specific impacts on accuracy.

The paper introduces DeepMM, a deep learning method to correct chimeric contigs in MAGs, improving gene annotation accuracy and reducing reference-discordant genes and virulence-factor calls.

PlantGeneAnn is a plant-optimized gene annotation tool that outperforms existing methods on a 20-species benchmark and enables efficient fine-tuning for non-model species.

LOCALE improves DNA sequence search by learning variation-robust embeddings, outperforming existing methods on benchmark datasets and real biological variance tasks.

REMAG improves eukaryotic genome recovery from metagenomic data by using non-contrastive self-supervised learning, outperforming existing tools in producing near-complete genomes.

The paper introduces genomeRxiv, a web server that enables precise strain-level identification of prokaryotes using ANI-based LINs, overcoming limitations of traditional taxonomic classification.

This study constructs a pangenome for deer mice to reveal extensive structural variation and its evolutionary implications.

NanoPlasmiQC provides a cost-effective workflow for full plasmid sequencing using ONT long-reads and automated data analysis.

The paper presents a chromosome-scale genome and transcriptomic atlas of taro corm development, identifying genes associated with carbohydrate metabolism and corm-specific expression.

The paper shows that horizontal gene transfers can reveal information about extinct and unsampled lineages in species trees through gene tree analysis.

The paper introduces the MSC-DL model to account for gene duplication, loss, and coalescence, showing it generates many more gene tree topologies than previous models.